Guided Stereo Matching. We also measure the boost in performance of a sensor fusion framework combining passive stereo with LiDAR sensors filtering raw data with our method. Purposely, we choose the guided stereo framework [1]} (since it does not explicitly take into account noise, differently from [2]}) and collect in Table REF  the accuracy yielded by PSMNet-ft-gd-tr – the model provided by the authors – on the 142 split and report the percentages of pixels with error larger than 2, 3, 4 and 5, together with MAE as in [1]}. While Surface [4]} can slightly improve all metrics by removing less than 5% of the total pixels with available LiDAR value, by filtering a more significant amount of pixels with our method, up to 25%, we can further improve and achieve the best accuracy.
Interestingly, the guided stereo framework has an opposite behavior with respect to depth completion, as it benefits more from strict filtering.
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